www.elsevier.com/locate/jom

Journal of Operations Management 25 (2007) 528–545

The relationships between supplier development, commitment,

social capital accumulation and performance improvement

Daniel R. Krause a,*, Robert B. Handfield b,1, Beverly B. Tyler b,2

a Department of Supply Chain Management, W.P. Carey School of Business, Arizona State University,

P.O. Box 874706, Tempe, AZ 85287-4706, USAb CB 7229, College of Management, North Carolina State University, Raleigh, NC 27695-7229, USA

Available online 3 July 2006

Abstract

This study investigates the relationships between U.S. buying firms’ supplier development efforts, commitment, social capital

accumulation with key suppliers, and buying firm performance. We identify linkages between supply chain management research

on supplier development and organization theory research on social capital to consider how buying firm commitment to a long-term

relationship, cognitive capital (goals and values), structural capital (information sharing, supplier evaluation, supplier develop-

ment), and relational capital (length of relationship, buyer dependency, supplier dependency) are related to buying firm performance

improvements (cost improvements, and quality, delivery, flexibility improvements). Analysis of buying firms from the U.S.

automotive and electronics industries provides support for the theory that buyer commitment and social capital accumulation with

key suppliers can improve buying company performance. Moreover, the findings suggest that the relationships of structural and

relational capital vary depending on the type of performance improvement considered.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Supply management; Purchasing; Supplier development; Social capital; Buyer–supplier relationship

1. Introduction

Previous research has shown that Japanese firms have,

at minimum, been able to gain temporary competitive

advantage from resource investments in supplier relation-

ships (Liker and Choi, 2004). However, the empirical

evidence is less complete for U.S. firms. Across the

various fields associated with organizational research

* Corresponding author. Tel.: +1 480 965 9859;

fax: +1 480 965 8629.

E-mail addresses: Daniel.Krause@asu.edu (D.R. Krause),

Robert_Handfield@ncsu.Edu (R.B. Handfield),

Beverly_Tyler@ncsu.Edu (B.B. Tyler).1 Tel.: +1 919 515 4674; fax: +1 919 515 6943.2 Tel.: +1 919 515 1652; fax: +1 919 515 6943.

0272-6963/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2006.05.007

there is growing recognition of the importance of inter-

organizational relationships as a source of competitive

advantage and value creation (Osborn and Hagedoorn,

1997; Powell, 1996; Smith et al., 1995). Using a social

capital lens, this study was initiated to better understand

the value created by U.S. firms willing to commit to long-

term relationships and to develop social capital with key

suppliers through supplier development.

The relationship between value creation and inter-

organizational relationships has been explored using

resource dependence theory (Pfeffer and Salancik,

1978), marketing channel theory (Frazier, 1983; Stern

et al., 1977); transaction cost economics (Williamson,

1985), transactional value analysis (Dyer, 1997; Zajac

and Olsen, 1993), resource-based theory (Tyler, 2001;

Wernerfelt, 1995), social capital theory (Granovetter,

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 529

1985; Jones et al., 1997; Tsai and Ghoshal, 1998), and

information processing theory (Hult et al., 2004). A

central proposition of these theories is that when

organizations invest in relation-specific assets, engage

in knowledge exchange, and combine resources through

governance mechanisms, a supernormal profit can be

derived on the part of both exchange parties. In this

study we leverage social capital theory to explain the

value created for buying firms committed to supplier

development.

One tangible form of inter-organizational exchange

that falls under the auspices of supply chain manage-

ment research is a practice initiated by industrial firms

called ‘‘supplier development.’’ Supplier development

is any activity initiated by a buying organization1 to

improve the performance of its suppliers (Krause et al.,

1998). Supplier development is an important strategy

for examination because it encapsulates two of the most

evident features of social capital: shared knowledge and

shared asset investments. Supplier development may

include goal setting, supplier evaluation, performance

measurement, supplier training, and other related

activities. Although this type of activity has been

prevalent in Japanese and Korean firms for a number of

years, it has been less evident in U.S. firms, or at least,

less studied (Krause and Handfield, 1999; MacDuffie

and Helper, 1997; MacDuffie, 1995). Perhaps U.S. firms

have been reluctant to invest in supplier development

due to a perceived lack of immediate return on

investment associated with deploying the resources

required to make it successful (Liker and Wu, 2000;

Dyer and Nobeoka, 2000; Smock, 2001). Alternatively,

perhaps U.S. firms work in different ways to improve

supplier performance.

This research was undertaken to better understand

the nature of supplier development efforts in the U.S.

and to better understand the specific form of returns

gained from investments by U.S. firms in supplier

development activities. The results of this study

provide two principal contributions to the extant

literature. First, we argue, and subsequently demon-

strate, that supplier development can conceptualized

through a social capital theory lens, and that this effort

provides valuable insights into the different dimensions

of social capital as they pertain to relationships between

industrial buying firms and their suppliers. Second, the

results indicate that the importance of the dimensions

1 The terms buying organization, buying firm and buyer are used

interchangeably throughout this paper to refer to industrial firms in

their role of purchasing inputs from suppliers.

of social capital varies depending on the type of buyer

performance improvements being emphasized, either

in the form of cost and total cost, or in terms of quality,

delivery and flexibility. More broadly, the paper

provides important insights into the relationship

between buyer social capital commitments and buyer

value creation.

The remainder of the paper briefly reviews the

literature on supplier development, buyer performance

goals, and the three types of social capital buying firms

may establish with key suppliers to improve buyer

performance: cognitive capital, structural capital, and

relational capital. Next, we draw associations between

supplier development practices and the different

dimensions of social capital, and develop a set of

hypotheses that identify relationships between the three

types of social capital and buyer performance improve-

ments. In the following sections, we describe the data,

the measures, and the analysis. Finally, we present the

results and discuss implications for further research.

2. Supplier development

The term ‘‘supplier development’’ was first used by

Leenders (1966) to describe efforts by manufacturers to

increase the number of viable suppliers and improve

suppliers’ performance. More specifically, supplier

development has been defined as any effort by an

industrial buying firm to improve the performance or

capabilities of its suppliers (Krause et al., 1998). The

practice of supplier development in Japan and its

application globally has been well documented (Asa-

numa, 1989; Clark and Fujimoto, 1991; Turnbull et al.,

1992). Interestingly, the practice was documented early

in the 1900s in the U.S. automotive industry when Ford

sought to improve suppliers’ capacity and performance

(Seltzer, 1928).

At about the same time supply chain management

researchers began discussing supplier development,

organizational theorists began arguing that complex-

product industries tend to be characterized by a high

degree of reciprocal interdependence on the part of

intermediate component makers and final assemblers

(Pfeffer and Salancik, 1978; Thompson, 1967). More

recently they have also recognized that investments in

relation-specific assets and knowledge sharing routines

are often necessary to coordinate non-routine tasks that

are reciprocally interdependent (Celly et al., 1999;

Clark and Fujimoto, 1991). Examples of industries

that fit these characteristics include automobiles,

aircraft, electronics, heavy machinery, machine tools

and robotics.

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545530

Recent developments associated with the relational

view of the firm are aligned with the practice of supplier

development (Dyer, 1996a, 1996b, 1997; Dyer and

Singh, 1998; Madhok and Tallman, 1998). According

to the relational view, investments are made by buyers

in the development of suppliers in order to accrue

tangible benefits such as reduced cost, greater quality

and flexibility, and more reliable delivery. In these

situations, the buying firm may arguably be prepared to

help the supplier through information sharing, techni-

cal assistance, training, and direct investment in

supplier operations, in return for the benefits of

improved performance and joint value creation (Zajac

and Olsen, 1993). In return, the supplier firm may be

expected to share information, dedicate human

resources to the improvement effort, and invest

in specific equipment.

From a relational perspective, buying firms must

determine what knowledge and resource investments

are likely to yield benefits. Moreover, appropriate

controls should be established to assure that these

investments are made. If the appropriate mechanisms

are not in place, the supplier may not perceive the

benefits associated with these investments, and may

reject the initiative to modify or improve their processes

(Krause et al., 1998). Furthermore, if a buyer asks a

supplier to invest in relation-specific assets but is not

willing to do the same, it is unlikely that the supplier

will be willing to make these investments and the

expected rents will not accrue.

Although the relational view of the firm is well

established in the supply chain literature, there is

comparatively little application of social capital theory

(Nahapiet and Ghoshal, 1998; Tsai and Ghoshal, 1998).

In order to extend current research and explain the value

created through U.S. buyer firm supplier development

initiatives, we chose to conduct a study focusing on the

types of social capital investments committed by

assemblers and component manufacturers in the U.S.

automotive and electronics industries to the subcom-

ponent manufacturers they have selected for supplier

development. Before we develop the hypotheses, we

briefly discuss the buying firm’s performance goals

driving these investments.

3. Buying firm performance

The fields of operations management and supply

chain management have established a commonly

agreed upon list of competitive priorities, which in

turn have become primary performance goals for

suppliers (e.g., Hayes and Wheelwright, 1984; Liker

and Wu, 2000; Monczka et al., 1998). Buying firms in

manufacturing industries, including automotive and

electronics, have four primary competitive priorities in

their end-markets: cost, quality, delivery time and

reliability, and flexibility (Ward et al., 1998). Moreover,

because these industries rely heavily on component

suppliers, the performance outcomes of buyers are

largely dependent on the performance outcomes of their

suppliers. If suppliers fail to perform, the end customer

is ultimately impacted.

3.1. Cost and total cost

Manufacturers in automotive and electronics pursue

lower costs of their supplied inputs, so as to lower their

total costs of final assembly and to provide a

competitive price on their final products to end

customers (MacDuffie, 1995). Improvements in the

cost of products for buying firms are dependent

partially on improvements by their subcomponent

suppliers, for example, on reductions in rework, scrap,

and downtimes. As suppliers reduce their costs, the

benefits should be at least partially transferred to their

industrial customers in the form of lower prices (Clark,

1989; Human and Provan, 1997; Turnbull et al., 1992).

In high-tech computer markets, producers increasingly

outsource production and distribution to suppliers in an

effort to reduce the cost of new technology. The trade

literature has recently highlighted companies’ efforts

to cut costs in the automotive industry by concentrating

on purchases from external suppliers that provide

inputs such as fuel and brake sub-systems (Dawson,

2001).

3.2. Quality

Quality has been a major focus of final assemblers

since the 1980s, when a significant gap existed between

Japanese and U.S. manufacturers. In the electronics

industry, product quality is a given, and six sigma

methodologies by companies such as Motorola have

become standard practice in the industry (Monczka

et al., 2000). Similarly, design-for-manufacturing

methodologies in the automotive industry have resulted

in quality being thought of as an order qualifier (Liker

and Wu, 2000; MacDuffie and Helper, 1997). However,

the quality of inputs from some suppliers is still

problematic, and the quality of component parts affects

customers’ perceptions of quality in the final product.

Some suppliers may not have adequate engineering and

technical resources for quality assurance, which

sometimes results in quality problems and production

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 531

delays (Human and Provan, 1997; Krause and Hand-

field, 1999).

3.3. Delivery

Delivery performance has two primary components:

(1) reliability of delivery, which is the ability to deliver

when promised, and (2) delivery speed, which is

typically thought of in terms of short delivery times

(Ward et al., 1998). Effective performance in both facets

of delivery, may partly explain why companies like Dell

have had success in reducing supply chain costs, such as

minimizing the amount of buffer inventory they must

hold.

3.4. Manufacturing flexibility

Firms are generally thought to respond to unpredict-

able environments through increased flexibility (Swa-

midass and Newell, 1987). Manufacturing flexibility

continues to be a concern for companies as they strive to

meet the changing needs of their customers. Electronics

firms want to avoid holding obsolete subcomponent

inventory for products when sales of those assembled

products drop at the end of their life cycle. Thus, a desired

outcome for buying firms is their ability to be more

flexible in responding to variations in end customer

demand (Jones et al., 1997). This outcome is being

driven, in part, by the need for greater mass-customiza-

tion of products (Clark and Fujimoto, 1991). Assemblers’

flexibility can be expected to be a function of their own

suppliers’ quality, delivery time, reliability, and flex-

ibility. In other words, suppliers must be able to meet

changes in quantity requirements, provide timely

delivery of products on short notice, and produce smaller

production runs at more frequent intervals (Dyer, 1996a;

Liker and Wu, 2000; Meredith, 2000; Womack et al.,

1990).

3.5. The effect of commitment on buying firm

performance

According to supply chain theory, performance

improvements sought by buying firms are often only

possible when they commit to long-term relationships

with key suppliers. Experience and research suggests

that when buying firms are unwilling to commit to long-

term relationships and to make investments to improve

suppliers’ performance, suppliers may be unwilling to

commit to resource investments that are relationship-

specific (Krause, 1999). Suppliers see relationship-

specific investments as vulnerable to opportunism

when resource commitments are not forthcoming from

the buying firm (Krause et al., 2000). However, when

buying firms signal a commitment to a long-term

relationship and indicate a willingness to make

investments in key suppliers to help them improve

performance, buyer performance would also be

expected to improve. These arguments suggest the

following hypothesis.

Hypothesis 1. There is a positive relationship between

buying firms’ commitments to long-term relationships

with key suppliers and buying firms’ performance

improvements.

While buyer performance goals and the value

associated with long-term commitments to key suppliers

are relatively well established in the supply chain

literature, the rationale for how buying firms invest

resources to improve performance of key suppliers and

their effect on buyer performance improvements is not

well understood. Building on supplier development and

social capital theories, we now develop hypotheses that

posit the relationship between buyers’ and key suppliers’

social capital accumulation and buyer performance

improvements.

4. Social capital theory

The organizational literature notes that social

capital is a valuable asset that stems from access to

resources made available through social relationships

(Granovetter, 1992). Nahapiet and Ghoshal (1998)

proposed three dimensions of social capital: struc-

tural, cognitive, and relational. They argued that the

structural dimension is related to social capital

resulting from the structural configuration, diversity,

centrality and boundary-spanning roles of network

participants. The cognitive dimension of social capital

refers to the resources that provide parties with shared

representations, interpretations, and systems of mean-

ing. They also suggested that shared meanings, such

as shared values and goals, develop through an

ongoing and self-reinforcing process of participation

in sense making processes as the parties construct a

shared understanding (Weick, 1995). Finally, Naha-

piet and Ghoshal suggested that the relational

dimension refers to personal relationships that

develop through a history of interactions, i.e., the

extent to which trust, obligation and reciprocity exist

between the parties.

The impact of social capital on performance has been

studied at multiple levels using different performance

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545532

measures. Some researchers have focused on relational

ties (structural capital) (e.g., Burt, 1992, 2000; Walker

et al., 1997), while others have considered the strength

of those ties (relational capital) (e.g., Granovetter, 1973,

1985; Hansen, 1999). Some researchers have consid-

ered both. For example, Moran (2005) examined the

impact of managers’ structural and relational capital on

their performance. He found that structural capital

played a stronger role in explaining execution-oriented

managerial tasks while relational capital played a

stronger role in explaining innovation-oriented tasks,

and encouraged future research to consider the effects

of both on a variety of performance measures. However,

empirical social capital research has seldom considered

the impact of cognitive capital, in terms of shared values

and goals, on firm performance. We will draw from the

social capital literature to hypothesize the relationships

between dimensions of social capital and buyer

performance improvement.

Organizational scholars posit that alliance partners’

investments in inter-firm knowledge-sharing routines

result in value creation (Dyer and Singh, 1998; Grant,

1996; Tyler, 2001). Regarding supplier development,

such routines are fundamental to any supplier improve-

ment effort initiated by a buying firm. Knowledge

shared by buying firms includes both the transfer of

factual knowledge, such as sharing of production

schedules (Kogut and Zander, 1992), and the transfer

of tacit, ‘‘sticky’’ knowledge, such as technology

roadmaps and shared values (Szulanski, 1996). Inkpen

and Tsang (2005) considered conditions that facilitate

knowledge transfer in strategic alliances. They argued

that knowledge transfer was enhanced when there were

long time horizons, high behavioral transparency and

multiple knowledge connections between partners, a

noncompetitive approach to knowledge transfer, goal

clarity, repeated exchanges, and frequent partner

interactions. In this paper, we consider many similar

factors in a supply chain setting.

4.1. Cognitive capital

Social capital theory suggests that cognitive capital

consists of the resources providing the parties with shared

representations, interpretations, and systems of meaning

(Nahapiet and Ghoshal, 1998). Tsai and Ghoshal (1998)

argued that within a firm cognitive capital is embodied in

a shared vision, i.e., collective goals and aspirations of the

parties, and is present when partners have similar

perceptions of common goals and how they should

interact. Inkpen and Tsang (2005) suggested that shared

goals and culture are the primary dimensions of cognitive

capital. They argued that goals are shared when members

of a network share a common understanding and

approach to achievement of network tasks and outcomes.

When goals and values are shared by buyers and their key

suppliers, continued interactions should result in an

ongoing and self-reinforcing process of participation in

sense making as the parties interact and socially construct

a shared understanding (Weick, 1995). In the context of

supplier development, this self-reinforcing process of

cooperative cognitive sense making can be expected to

improve buyer performance. If goals are shared, buyers

and suppliers can be expected to have a shared

understanding of what constitutes improvement and

how to accomplish it. This should lead to greater

improvement in cost, quality, delivery and flexibility.

If goals and values are incongruent, interactions

between the two parties can be expected to lead to

misinterpretation of events and conflict (Inkpen and

Tsang, 2005; Schnake and Cochran, 1985). As

misinterpretation and conflict intensifies, both parties

can be expected to become dissatisfied, and to limit

information sharing, resulting in negative effects on

productivity and performance. Linking this back to

supply chain research, Zaheer et al. (1998) found a

negative relationship between the level of buyer–

supplier conflict and supplier performance in the

electrical equipment manufacturing industry. Handfield

and Nichols (1999) argued that diverse views of quality

and timeliness should be resolved so that joint efforts of

buyers and suppliers can focus on necessary activities

and that shared meaning becomes a critical mechanism

to ensure coordination. Hult et al. (2004) found that in

supply chains, shared meaning is related to both

objective and subjective measures of cycle-time

reduction. These arguments suggest that when buyers

and their key suppliers have similar goals and values for

their relationship, cognitive capital will positively affect

performance.

Hypothesis 2. There is a positive relationship between

buying firms’ perceptions of shared values and goals

with key suppliers and buyers’ performance improve-

ments.

4.2. Structural capital

Bessant et al. (2003) concluded that the collectivity

and shared purpose associated with social capital help to

establish ‘appropriate practices’ between firms. Research

has suggested that practices may range from general

information sharing of codified information to the

sharing of tacit knowledge. Organization theory and

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 533

supply chain management research have recognized the

central role of information sharing to the acquisition of

capabilities through inter-firm ties, in general (Ahuja,

2000; Gulati, 1999; Stuart, 1998), and information

sharing with key suppliers, more specifically (Uzzi,

1997; Dyer and Nobeoka, 2000). Information sharing

in this literature has typically been defined as ‘‘the

degree to which each party discloses information that

may facilitate the other party’s activities’’ (Heide and

Miner, 1992: 275) and includes what we describe as

information sharing, supplier evaluation and more

‘‘direct involvement’’ supplier development activities

such as regular visit to suppliers’ facilities and supplier

training (Krause et al., 2000; McEvily and Marcus,

2005; Uzzi, 1997).

In collaborative buyer–supplier relationships, atti-

tudes toward learning are noncompetitive, which can be

expected to lead to greater symmetric learning than in

other forms of alliances (Inkpen and Tsang, 2005).

Furthermore, in a supplier development context we can

expect information exchanges between key suppliers

and buyers to be more detailed, intricate, and

proprietary than in arm’s-length relationships (Uzzi,

1996). Supplier development activities, especially those

dubbed ‘‘direct involvement’’ activities, are much more

complex than short-term contracting and as such buyer

performance should be improved by matching diverse

communication requirements with different methods of

information sharing (Krause et al., 2000; Brass et al.,

2004). Hansen (1999) notes that strong ties provide a

better conduit for the transfer and exchange of complex

issues and ideas. For example, a buyer and supplier

struggling to arrive at shared meanings may rely more

on rich media, such as site visits or co-location of

employees in order to facilitate resolution of various

perceptions and to effectively transmit emotions and

subtleties (Daft and Lengel, 1986; Hult et al., 2004;

Nonaka, 1994).

Thus, different supplier development efforts may be

associated with different means of information sharing.

When information is codified, knowledge related to

tangible resources and their meaning is generally agreed

upon and understood, and information can be shared

using communication technology (Moran, 2005). Exam-

ples of information that is relatively easily interpreted and

that can be easily transferred through computing and

communication technologies, include uncertainty in

market demand, raw materials supply, tariffs, and

supplier performance data (Lin et al., 2002; Reed and

Walsh, 2002). Moreover, supplier evaluations and audits,

providing performance feedback to suppliers, and

supplier certification, should provide both the buyer

and supplier with important information exchange that

should ultimately help buyers improve their own

performance.

In addition to the above, buying firms committed to

‘‘direct involvement’’ supplier development activities

provide more personal, face-to-face interactions with

their suppliers and thus should be more successful in

transferring tacit knowledge and accrue performance

improvements as a result of their investments because

the ambiguity of tacit knowledge requires thicker

information exchange (Lawson et al., 2006; Moran,

2005). Thus, buying firms that engage in ‘‘direct

involvement’’ supplier development to transfer tacit

knowledge may include such activities as regular site

visits by buyer personnel, training of the supplier’s

employees, and a dedicated supplier development team

(Krause et al., 2000).

The extant research has suggested that future

research should consider how supplier development

activities vary across different performance goals

(Krause et al., 2000). The knowledge sharing

activities necessary for lowering the buying firm’s

costs, are arguably not the same as might be required

to transfer tacit knowledge to improve quality,

delivery, and flexibility performance—the latter three

being more related to process and product innovation

(McEvily and Marcus, 2005; Moran, 2005). Sharing

of information such as the results of supplier

evaluation could be expected to provide the social

capital accumulation most relevant to cost perfor-

mance improvements. Because more intense supplier

development activities require more human capital

commitment than is required for sharing more easily

codified information, the costs associated with them

could easily be greater than the value they might

provide the buyer (Daft et al., 1993). In contrast,

improvements in quality, delivery, and flexibility are

more likely to require buyers’ commitment to more

intensive supplier development efforts. These

improvement goals may require more personal

interaction, discussion and common experiences,

which allow for clarification of issues and

the establishment of shared understandings of

ambiguous information (Daft and Lengel, 1984,

1986; Thomas and Trevino, 1993; Hansen, 1999;

Brass et al., 2004).

To summarize, structural capital investments and

accumulations can be expected to improve buyer

performance. However, the effects of various types of

structural capital can be expected to differ according to

the type of performance improvements sought. The

theory presented suggests that basic information sharing

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545534

and supplier evaluation should be more positively

related to improvements in buyer costs than other

supplier development efforts where tacit knowledge

exchange is necessary. Furthermore, supplier develop-

ment initiatives that focus on more personal forms of

communication that entail the transfer of tacit knowl-

edge will be more positively related to buyer

improvements in quality, delivery speed and reliability,

and flexibility than simple information sharing or

supplier evaluation. Thus, we propose the following

hypotheses.

Hypothesis 3a. There are stronger positive relation-

ships between buyers’ efforts to share information and

evaluate suppliers to achieve buyers’ cost performance

improvements, than between buyers’ ‘‘direct involve-

ment’’ supplier development activities and cost

improvements.

Hypothesis 3b. There is a stronger positive relation-

ship between buyers’ ‘‘direct involvement’’ supplier

development activities with key suppliers to achieve

buyers’ performance improvements in quality, delivery,

and flexibility—than between buyers’ efforts to share

information and evaluate suppliers, and these perfor-

mance improvements.

4.3. Relational capital

The extant relational capital literature has argued

that as the level of interaction between alliance partners

increases, organizational routines are established

(Nelson and Winter, 1982), and the investment in co-

specialized assets and level of bilateral dependence also

increases (Teece, 1986). Co-specialization is believed to

be the result of investments in skills and routines

adapted to the exchange and the development of social

relationships among partners (Levinthal and Fichman,

1988). Experience with a partner is said to raise

collaborative expectations and stimulate learning and

readjustment cycles as the relationship evolves (Doz,

1996). For example, Reuer et al. (2002) argued that

partner-specific experience facilitates ex post adjust-

ments in alliance monitoring mechanisms, which

suggests that prior ties facilitate adjustment as a

consequence of familiarity and the development of

inter-organizational routines.

Previous researchers have argued that trust tends to

increase with the length of the relationship between

buyers and suppliers (Helper, 1991; Sako and Helper,

1998). Previous research has found that repeated

partner-specific ties have a stronger effect on knowledge

accumulation than does repeated technology-specific or

repeated general experience ties, and that non-equity

based alliances are more tightly coupled to the number

of previous ties between partners than equity based

alliances (Gulati, 1995a; Reuer et al., 2002).

Furthermore, a prior history of cooperation between

firms has been found to reduce their expectations of

opportunism (Parkhe, 1993) and decrease their percep-

tions of exchange hazards (Deeds and Hill, 1998).

Building on this stream of research, Ring and Van de

Ven (1994) and Gulati (1995a) noted that past

transactions may alter the calculus for further transac-

tions since a history of interaction decreases the

expected cost of dealing with suppliers. These argu-

ments have been extended to suggest that relational

norms established through prior exchanges substitute

for complex, explicit contracts or vertical integration

(Dyer and Singh, 1998; Gulati, 1995b). Through

repeated interactions the parties appear to develop trust

in one another such that they may no longer need to rely

on formal contacts to ensure performance (Zaheer and

Venkatraman, 1995).

Hoetker (2005) investigated how interactions

improve communication between buyers and suppli-

ers. He argued that relationship-specific communica-

tion and coordination routines develop over time

(Mitchell and Singh, 1996), partners with first-hand

knowledge of each other’s capabilities are more

effective in assigning tasks to the most capable party

(Fichman and Levinthal, 1991), and that through

multiple interactions buyers and suppliers develop a

common language for discussing technical and design

issues (Buckley and Casson, 1976). Hoetker (2005)

argued that first hand knowledge of a partner’s past

behavior provides information and that due to past

interactions exchange partners are less likely to act

opportunistically for social, psychological, and eco-

nomic reasons (Crocker and Reynolds, 1993; Gran-

ovetter, 1995). Furthermore, he suggested that trust

develops between individuals as they engage in

repeated transactions and it becomes institutionalized,

leading to trust between organizations that endures

despite changes in the individuals involved (Zaheer

et al., 1998).

Research on buyer–supplier relationships has also

found that that cooperation increases with a higher

frequency of contact in the relationship (Heide and

Miner, 1992), and that trust between buyers and

suppliers increases the longer they work together

(Helper, 1991). Furthermore, Stuart et al. (1998)

suggested that cost reductions and the development of

problem solving capabilities are the main benefits

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 535

accrued. Thus, in the context of supplier develop-

ment, it can be argued that relational capital, as

represented by the years of the buyer–supplier

relationship and the dependency of the buyer

and the supplier to the relationship, can be expected

to be positively related to buyer performance

improvement.

Hypothesis 4a. There is a positive relationship

between the length of buying firms’ relationships with

key suppliers and buyers’ performance improvements.

Hypothesis 4b. There is a positive relationship

between buying firms’ perceptions of buyer and sup-

plier dependency on the relationship and buyers’ per-

formance improvements.

5. Methods

We collected data from purchasing executives

employed by firms in the automotive and electronics

industries with prior experience in improving a key

supplier’s performance. We also collected data from a

subset of these key suppliers. Not all of the firms

represented were direct producers of automobiles or

electronics; as such, they may be listed under different

industrial codes. However, their final customers were

automobile assemblers or electronics assemblers, and

thus they are part of an automobile or electronics supply

chain.

The Institute for Supply Management (ISM)

continues to use SIC codes, and provided us with a

list of their title 1 members employed by firms in the

electronics industry, SIC code 36. Title 1 ISM members

are purchasing executives with titles such as director

and manager. Subsequently, we drew a random sample

of 750 names from that list. A sample of executives

working for firms in the automotive industry was also

targeted using a four-digit SIC code within the U.S.

automotive industry—Motor Vehicle Parts & Acces-

sories (SIC 3714). A comprehensive database of 2945

U.S. manufacturing facilities with this SIC code was

obtained from Elm International, in East Lansing, MI.

A random sample of 750 names was drawn the list,

which included contact information for purchasing

executives.

A buyer questionnaire was mailed to each of the

1500 purchasing executives in the electronics and

automotive sub-samples. The questionnaire asked

respondents to report on their firm’s relationship

with one supplier that they had worked with to

improve performance. At the end of the question-

naire respondents were asked to share the contact

information of a key contact at the supplier firm. This

request resulted in contact information for 124

supplier firms from the 392 responses received from

the buying firms. Nineteen surveys were set aside from

the analysis because of incomplete information; thus

the effective response rate was approximately 25%. To

encourage responses, a variation of Dillman’s tailored

design method was used (Dillman, 2000). An initial

mailing of surveys was followed 10 days later by

reminder postcards. Twenty-nine days after the initial

mailing, a second wave of surveys was sent to non-

respondents.

Although there is no generally accepted minimum

percentage for response rates, non-response bias is

always a concern. One method for testing non-response

bias is to test for significant differences between the

responses of early and late waves of returned surveys

(Lambert and Harrington, 1990). This approach is based

on the assumption that late responders are somewhat

representative of the opinions of non-respondents. For

the present study, twenty of the survey items used for the

analysis were randomly selected from the buyer survey,

two groups of seventy surveys were chosen from the

first and last waves of surveys received, and t-tests were

performed on the responses of the two groups. The t-

tests yielded no statistically significant differences

among the twenty survey items tested. Although these

results do not rule out non-response bias, they suggest

that non-response may not be a problem to the extent

that late responders represent the opinions of non-

respondents.

5.1. Supplier data

A survey was mailed to the supplier contact with a

letter describing the purpose of the study and

identifying the buyer respondent who had provided

their contact information. They were asked to complete

a questionnaire that was similar to the buyer’s, and were

assured of strict confidentiality. Seventy-five useable

supplier questionnaires were returned; thus the effective

response rate for the supplier sample was approximately

sixty percent.

This set of 75 supplier surveys provided a dyadic

data set for a subset of the buying firms. Because the

dyadic data set was small, and the questions asked of the

supplier were a subset of the questions asked of the

buying firm respondent, the use of this data was limited

for the present paper. However, correlations were run on

a few items that were common across the two surveys.

For example, we asked the buyer and supplier

respondents about their level of agreement with the

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545536

following two statements: (1) We expect to be working

with this supplier [customer] for the foreseeable future,

and (2) our relationship with this supplier [customer] is

long-term in nature. The correlation between these two

combined items across the buyer and supplier dyads

was 0.35, and significant at p < 0.01 (n = 74). This

result provides some indication that the two parties

shared similar perceptions of the relationship.

5.2. Dependent variables

Two distinct sets of dependent variables were

identified. The notion of competitive priorities in

operations, purchasing and supply chain management

provides four primary factors: cost, quality, delivery and

flexibility, with some researchers adding innovation as a

fifth factor (Krause et al., 2001; Ward et al., 1990,

1998). A set of single-item scales asked buying

company respondents to indicate the effect of supplier

development on the performance of the buying firm’s

own products, in terms of cost, total cost, product

quality, delivery times, delivery reliability, flexibility

and other factors. Each of these items was measured on

a seven-point Likert scale, where 1 = strongly agree,

4 = neutral and 7 = strongly disagree. These items were

evaluated using an exploratory factor analysis, as shown

in Appendix 1. The cost and total cost items clearly

loaded together forming one factor. Similarly, the

quality, delivery and flexibility items also loaded

together as one factor.

5.3. Independent variables

The independent variables incorporated into the

analysis included buyer commitment, shared values,

information sharing, supplier evaluation, ‘‘direct invol-

vement’’ supplier development activities, length of

relationship, supplier dependence, and buyer depen-

dence. Appendix 2 provides the survey items. All scale

items were measured using a seven-point Likert scale

where 1 = strongly agree, 4 = neutral, 7 = strongly

disagree, except as noted.

5.3.1. Buyer commitment

Relationship commitment is a common measure

used in examining dyadic supply chain relationships.

Performance improvements sought by buying firms are

often only possible when they commit to a long-term

relationship with their key suppliers (Krause, 1999).

The factor was measured using two questions which

tapped into the concept of relationship continuity

(a = 0.84).

5.3.2. Shared values

Three scale items comprise the scale for shared

values (a = 0.84). These three items tap well into the

idea that goals and values may be shared by buyers and

their key suppliers (Weick, 1995).

5.3.3. Information sharing

Effective information sharing is believed to be an

essential antecedent to the buying firm’s involvement in

supplier development (Krause, 1999). Effective inter-

organizational communication may be characterized as

varying along some or all of the following dimensions:

frequency, degree of formality, level of willingness to

share proprietary information, and timeliness (Heide

and Miner, 1992). In this study, buying firm respondents

were asked to specify the extent of their willingness to

share information with the supplier. Information sharing

was measured with three scale items (a = 0.72).

5.3.4. Supplier evaluation

The items measuring supplier evaluation include

formal evaluation, feedback of the evaluation results,

and the use of supplier certification, the latter being a

form of evaluation with a focus on processes. The first

two of these items is similar in wording to those used by

Krause et al. (2000) to measure the factor they called

supplier assessment (a = 0.77).

5.3.5. Supplier development

Supplier development activities vary in terms of the

degree of involvement of the buying firm with the

supplier. Krause et al. (2000) differentiated supplier

development activities that were internalized by the

buying firm and thus involved direct involvement of the

buying firm’s personnel, from other supplier develop-

ment ‘‘hands-off’’ activities that did not involve

significant personnel time investments. We have taken

a similar ‘‘direct involvement’’ approach in the present

study. Thus, the measures used for supplier develop-

ment focus on direct involvement activities, specifically

the allocation of personnel to improve the supplier’s

skill base, regular visits to the supplier by the buyer’s

engineers, and dedicated supplier development teams

(a = 0.75; seven-point Likert scales with 1 = exten-

sively, 4 = somewhat, and 7 = very little).

5.3.6. Length of relationship

The length of relationship variable was a single,

open-ended question which asked respondents:

‘‘approximately how long has your company been

purchasing from this supplier?’’ The question specifi-

cally asked for the length of the relationship in years.

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 537

Table 1

Titles of buying firm respondents

Titles Frequency Percentage

Purchasing manager 122 33.1

Materials manager 42 11.4

Purchasing agent 41 11.1

Director of purchasing/sourcing 31 8.4

Senior buyer 25 6.8

Buyer 13 3.5

Vice-president 11 3.0

Commodity manager 13 3.5

Director of materials management 10 2.7

Miscellaneous titles 61 16.5

369a 100.0b

a Frequency missing = 5.b Of those respondents that reported.

Table 2

Respondents’ sales

Companies’ annual gross

sales dollars

Frequency Percentage

Less than $ 1 million 1 0.3

$ 1–5 million 15 4.3

$ 5–10 million 16 4.5

$ 10–50 million 107 30.4

$ 50–100 million 62 17.6

$ 100–500 million 89 25.3

$ 500–1 billion 16 4.5

Over $ 1 billion 46 13.1

352a 100.0b

a Frequency missing = 22.b Of those respondents that reported Sales.

5.3.7. Buyer dependence

Buyer dependence was investigated using four

questions that examined how unproblematic the supplier

was to replace, perceptions of how many suppliers were

available, and whether finding a new supplier might

require a redesign of the purchased part (a = 0.81).

5.3.8. Supplier dependence

Supplier dependence was measured from the buying

firm’s perspective, asking the respondents how depen-

dent they perceived the supplier to be on their firm’s

business. In our experience of gathering case data on

supplier development, we have found that buying firm

representatives typically know how dependent a

supplier is on them for its business. Many firms have

explicit guidelines regarding numerical limits on how

much of a supplier’s output to purchase. These policies

are typically in place so as to limit suppliers’

dependence. Thus, these items asked how easy it might

be for the supplier to look elsewhere for business if they

stopped purchasing from them (a = 0.74).

5.4. Control variables

We controlled for industry with two environmental

variables. The respondent sample included firms that

were part of either the electronics or automotive

industries. Thus, the sample was not very heterogeneous

with respect to the destination of their products. Despite

this relative homogeneity, we used perceptual measures

that focused on the rate of obsolescence and the relative

change of technology in the industry. Because supplier

development efforts use firms’ resources, we felt that

larger firms might be likely to engage in these efforts

and thus controlled for size by using annual sales as a

surrogate.

6. Results

The industries represented in the buying firm sample

included automotive (n = 173), electrical equipment

and electronics (n = 70), industrial machinery (n = 59),

miscellaneous manufacturing (n = 61), and not reported

(n = 11). Supplier respondents were a diverse group

with industrial machinery (n = 34), metal products

(n = 13), transportation equipment (n = 4), electronics

(n = 7), other manufacturing (n = 11), and non-manu-

facturing (n = 6). The buying firm respondents were

comprised of executives with titles including director of

purchasing, purchasing manager, materials manager,

senior buyer, commodity manager, and similar titles, as

shown in Table 1. The respondent firms’ gross annual

sales are reported in Table 2—the sample is heavily

populated by larger firms.

Descriptive statistics and correlations of the vari-

ables and factors are provided in Table 3. The average

length of the relationship reported on by the buying firm

respondents was approximately 12.5 years. The

remaining variables in Table 3 are summated variables.

Additional information on the variables is provided in

Appendices 1 and 2, which provide the specific wording

of the scale items, the results of the exploratory factor

analysis, and Cronbach alpha for each set of scale items.

The exploratory factor analysis resulted in clean factor

loadings for the various factors. A small number of

survey items were thrown out because of cross-loading

across factors.

6.1. Dependent variables: cost and total cost

Table 4 provides the results of the regressions for the

main effects of buyer commitment, shared values,

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545538T

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information sharing, supplier evaluation, supplier

development, relationship length, buyer dependence

and supplier dependence, on buyer performance as

measured in terms of cost and total cost. Model 1 is the

baseline model—the model was not significant and

none of the control variables was significant. Model 2

evaluated the impact of buyer commitment. The results

indicate that although the control variables were not

significant, buying firm commitment was highly

significant ( p < 0.01). This result indicates support

for Hypothesis 1.

Model 3, in Table 4, examined the impact of shared

values, information sharing, supplier evaluation,

supplier development, length of relationship, buyer

dependence and supplier dependence, in addition to

the control variables and buyer commitment. The

control variable of environmental dynamism was

significant ( p < 0.10), as was buyer commitment

( p < 0.01) which provides additional support for

Hypothesis 1. The results of Model 3 indicate that

shared values (Hypothesis 2; p < 0.01), buyer

dependence (Hypothesis 4b; p < 0.01), and supplier

dependence (Hypothesis 4b; p < 0.10) were all

significant which provides support for Hypotheses 2

and 4b. The variables of information sharing, supplier

evaluation, supplier development (Hypothesis 3a), and

length of the relationship (Hypothesis 4a) were not

significant.

In summary, the analysis found support for

Hypotheses 1, 2 and 4b but not Hypothesis 3a or 4a,

when viewing buyer performance as it pertains to cost

and total cost.

6.2. Dependent variables: quality, delivery and

manufacturing flexibility

Table 5 reports the main effects of buyer commit-

ment, shared values, information sharing, supplier

evaluation, supplier development, length of relation-

ship, buyer dependence, and supplier dependence, on

the dependent factor of buyer performance, defined in

terms of quality, delivery and manufacturing flexibility.

Model 1, including only the control variables, was not

significant; thus there were no significant effects for

environmental dynamism, annual sales, or annual sales

squared.

Model 2 examined the effects of the control variables

and buyer commitment. The overall model was

significant, as was the buyer commitment variable

( p < 0.01). This result provides additional support for

Hypothesis 1.

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 539

Table 4

Regression analysis for performance: cost, total cost

Independent variables Cost total cost

Model 1 Model 2 Model 3

Constant 7.322 (1.337) 5.752 (1.286) 4.531 (1.487)

Environment: dynamism �0.030 (0.050) �0.045 (0.047) �0.083* (0.047)

Annual sales �0.542 (0.488) �0.520 (0.460) �0.516 (0.452)

Annual sales � annual sales 0.038 (0.045) 0.039 (0.042) 0.033 (0.041)

Buyer commitment 0.390*** (0.063) 0.274*** (0.071)

Shared values 0.248*** (0.045)

Information sharing �0.073 (0.051)

Supplier evaluation 0.026 (0.029)

Supplier development 0.010 (0.030)

Length of the relationship in years 0.022 (0.014)

Buyer dependence 0.056*** (0.020)

Supplier dependence �0.044* (0.024)

Adjusted R2 0.00 0.10 0.20

F 1.19 n.s. 10.70*** 8.47***

* p < 0.10.*** p < 0.01.

The variables that measure the various effects of

social capital were included in Model 3. This model was

significant overall, with an adjusted R2 of 0.30. The

control variable of annual sales was negative and

moderately statistically significant ( p < 0.10). Addi-

tional significant variables included buyer commitment

( p < 0.01), shared values ( p < 0.01), and supplier

development ( p < 0.01)—these results indicate support

for Hypotheses 1, 2 and 3b. The remaining variables of

Table 5

Regression analysis for performance: quality, delivery, manufacturing flexi

Independent variables Quality delivery man

Model 1

Constant 13.138 (2.364)

Environment: dynamism 0.114 (0.089)

Annual sales �1.229 (0.863)

Annual sales � annual sales 0.095 (0.079)

Buyer commitment

Shared values

Information sharing

Supplier evaluation

Supplier development

Length of the relationship in years

Buyer dependence

Supplier dependence

Adjusted R2 0.00

F 1.62 n.s.

* p < 0.10.*** p < 0.01.

information sharing, supplier evaluation, length of

relationship, buyer dependence and supplier depen-

dence were not significant, indicating no support for

Hypothesis 4a or 4b.

To summarize the results, overall, from the

analyses represented in Tables 4 and 5, support was

found for Hypotheses 1, 2, 3b and mixed support for

Hypothesis 4b. No support was found for Hypothesis

3a or 4a.

bility

ufacturing flexibility

Model 2 Model 3

10.065 (2.208) 4.789 (2.471)

0.088 (0.082) 0.056 (0.079)

�1.230 (0.790) �1.280* (0.751)

0.100 (0.073) 0.113 (0.069)

0.784*** (0.108) 0.495*** (0.119)

0.549*** (0.075)

�0.075 (0.087)

0.057 (0.049)

0.146*** (0.050)

0.039 (0.023)

0.002 (0.034)

�0.004 (0.040)

0.14 0.30

14.91*** 13.88***

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545540

7. Discussion

The present research indicates that social capital is a

promising theory for supply chain research, with its focus

on creating and sharing knowledge across organizations

(Nahapiet and Ghoshal, 1998). Hult et al. (2004) argued

that future research would benefit from using a variety of

organizations and social capital outcomes such as quality,

cost and flexibility. The results of this research indicate

support for the application of social capital theory to

buyer–supplier relationships in the context of supplier

development. The present work also reinforces the notion

that the different dimensions of social capital, in terms of

structural embeddedness, relational embeddedness, and

the cognitive dimension are useful explanatory con-

structs that deserve more investigation in a supply chain

context.

To restate our findings, we examined buyer–supplier

relationships through a social capital lens, with a

specific focus on buyer performance achievements

gained through supplier development. Our findings

indicate that commitment between the two firms is an

important complementary condition to establishing

performance goals, and provides value to buying firms

that seek social capital accumulation with suppliers.

Further, our findings suggest that the different dimen-

sions of social capital have unique effects depending on

performance goals: cost and total cost, versus quality,

delivery, and flexibility.

Specifically, cognitive capital in the form of shared

values, and relational capital in the form of buyer and

supplier dependence, were important in explaining

buyer performance achievements in cost and total cost.

In contrast, in explaining buying firm performance in

terms of quality, delivery and flexibility, cognitive

capital in the form of shared values, and structural

capital in the form of supplier development activities

were more important. Common explanatory factors for

both dimensions of performance included commitment

to the relationship and cognitive capital.

Performance outcomes in quality, delivery and

flexibility appear to depend more on ‘‘direct involve-

ment’’ supplier development activities than cost

performance outcomes. We measured direct involve-

ment supplier development in terms of allocating buyer

personnel to improve the supplier’s technical skill base,

a dedicated supplier development team, and regular

visits to the supplier by the buying firm’s engineering

personnel. The type of interaction implied in these items

would indicate an environment that facilitates the

transfer of tacit knowledge between the two firms and

facilitates learning.

Improvements in both dimensions of performance

are likely to require shared values and goals, and these

could also be communicated more accurately in the

face-to-face interactions that take place over time with

dedicated teams visiting the supplier’s facilities—

however, only the quality, delivery and flexibility

performance dimension was significant for supplier

development activities. Cost and total cost concerns

may be more aptly addressed at the negotiation table

during periodic contractual negotiations, than quality,

delivery and flexibility concerns, or at least be

accomplished without the in-depth communication that

takes place during supplier development visits.

We did not find support for the effects of information

sharing and supplier evaluation on either type of

performance. Clearly, information sharing is incorpo-

rated into any relationship, and is an important part of

the supplier development factor as we measured it.

Thus, as argued earlier, the information sharing that

takes place in ‘‘direct involvement’’ supplier develop-

ment may be more conducive to sharing tacit

information, and the results of the analysis provide

support for this notion. Hult et al. (2004) encouraged

future research to further articulate the influence of

information within the social capital context where

shared meanings may mediate effects of information

distribution activities. Subsequent research efforts will

hopefully revisit information sharing, both the inter-

personal information investigated in this paper, and

impersonal types such as information technology.

Nahapiet and Ghoshal (1998) noted the importance

of interdependence on the development of social

capital, but our findings indicate that interdependence

was only significant for the cost performance factor. Our

measure of relationship length was not significant for

either type of performance improvement. Thus, future

research could also bring in trust and attempt to

distinguish trust, measurement-wise, from the notion of

shared values and goals.

8. Conclusion

The significant increase in outsourcing over the past

two decades has fueled researchers’ interest in the

benefits of buyer–supplier relationships. As cooperation

and collaboration between buyers and suppliers has

increased, the performance of these relationships, and

the fact that there are socially embedded dimensions

should be of interest to researchers. However, knowl-

edge is limited in terms of the different dimensions of

social capital and their unique contributions to the

various dimensions of performance.

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 541

The literature in strategy and organizational theory

has examined social capital for some time, but the

applications in supply chain research are relatively

limited. Inkpen and Tsang (2005) argued that we need

to examine in detail the characteristics of different

network types. This study is in response, in part, to their

call. We have taken a special type of strategic alliance –

supplier development initiatives by buying firms – and

sought to study dimensions of cognitive, structural and

relational capital. We found support for their suggestion

that different types of knowledge types have different

effects on organizational processes and that tacit

knowledge requires more intimate personal interaction

than more codified and easily understood knowledge.

Appendix A. Exploratory factor analysis of dependent v

Thus, the present study provides some initial

understanding of industrial buyer–supplier relationships

and how their social capital dimensions relate to buying

firm performance. We believe more research is needed.

Specifically, future efforts could focus on existing

measures of the three dimensions of social capital, and

on additional measures of buying firm performance

such as innovation. Compared to the transaction cost

economics perspective that prevails in the extant supply

chain literature, social capital offers an opportunity for

increased understanding of the complexities of supply

chain relationships. We hope other researchers will

further investigate the social dimensions of these

relationships.

ariables only

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545542

Ap

pen

dix

B.

Ex

plo

rato

ryfa

cto

ra

na

lysi

sre

sult

s—in

dep

end

ent

fact

ors

D.R. Krause et al. / Journal of Operations Management 25 (2007) 528–545 543

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